A family of biologically-inspired Convolutional Neural Networks (CNNs). VOneNets have the following features:

• Fixed-weight neural network model of the primate primary visual cortex (V1) as the front-end.
• Robust to image perturbations
• Brain-mapped
• Flexible: can be adapted to different back-end architectures

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(Click on model names to download the weights of ImageNet-trained models. Alternatively, you can use the function get_model in the vonenet package to download the weights.)

VOneNets was trained with images normalized with mean=[0.5,0.5,0.5] and std=[0.5,0.5,0.5]

More information coming soon...

Current state-of-the-art object recognition models are largely based on convolutional neural network (CNN) architectures, which are loosely inspired by the primate visual system. However, these CNNs can be fooled by imperceptibly small, explicitly crafted perturbations, and struggle to recognize objects in corrupted images that are easily recognized by humans. Recently, we observed that CNN models with a neural hidden layer that better matches primate primary visual cortex (V1) are also more robust to adversarial attacks. Inspired by this observation, we developed VOneNets, a new class of hybrid CNN vision models. Each VOneNet contains a fixed weight neural network front-end that simulates primate V1, called the VOneBlock, followed by a neural network back-end adapted from current CNN vision models. The VOneBlock is based on a classical neuroscientific model of V1: the linear-nonlinear-Poisson model, consisting of a biologically-constrained Gabor filter bank, simple and complex cell nonlinearities, and a V1 neuronal stochasticity generator. After training, VOneNets retain high ImageNet performance, but each is substantially more robust, outperforming the base CNNs and state-of-the-art methods by 18% and 3%, respectively, on a conglomerate benchmark of perturbations comprised of white box adversarial attacks and common image corruptions. Additionally, all components of the VOneBlock work in synergy to improve robustness. Read more: Dapello*, Marques*, et al. (biorxiv, 2020)

• Python 3.6+
• PyTorch 0.4.1+
• numpy
• pandas
• tqdm
• scipy

Dapello, J., Marques, T., Schrimpf, M., Geiger, F., Cox, D.D., DiCarlo, J.J. (2020) Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. biorxiv. doi.org/10.1101/2020.06.16.154542

GNU GPL 3+

Soon...

1. You need to clone it in your local repository $ git clone https://github.com/dicarlolab/vonenet.git

2. And when you setup its codes, you must need 'val' directory. so here is link. this link is from Korean's blog I refered as below https://seongkyun.github.io/others/2019/03/06/imagenet_dn/

   ** Download link**
   

https://academictorrents.com/collection/imagenet-2012

Once you download that large tar files, you must unzip that files -- all instructions below are refered above link, I only translate it

$ mkdir train && mb ILSVRC2012_img_train.tar train/ && cd train $ tar -xvf ILSVRC2012_img_train.tar $ rm -f ILSVRC2012_img_train.tar (If you want to remove zipped file(tar)) $ find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done $ cd ..

$ mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar $ wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash

when it's finished, you can see train directory, val directory that 'val' directory is needed when setting up

after all execution above, must remove directory or file not having name n0000 -> there will be fault in training -> ex) 'ILSVRC2012_img_train' in train directory, 'ILSVRC2012_img_val.tar' in val directory

3. if you've done getting data, then we can setting up go to local repository which into you cloned and open terminal (you must check your versions of python, pytorch, cudatoolkit if okay then,) $ python3 setup.py install $ python3 run.py --in_path {directory including above dataset, 'val' directory must be in!}

$ python3 run.py --in_path {directory including above dataset, 'val' directory must be in!} --ngpus 0

ngpus is 1 as default. if you don't care running on CPU you do so